![]() ![]() setting the global SQL option to true.setting data source option mergeSchema to true when reading ORC files, or.Since schema merging is a relatively expensive operation, and is not a necessity in most cases, we Source is now able to automatically detect this case and merge schemas of all these files. Up with multiple ORC files with different but mutually compatible schemas. Users can start withĪ simple schema, and gradually add more columns to the schema as needed. Like Protocol Buffer, Avro, and Thrift, ORC also supports schema evolution. The vectorized reader is used when is also set to true, and is turned on by default. ![]() The vectorized reader is used for the native ORC tables (e.g., the ones created using the clause USING ORC) when is set to native and is set to true.įor the Hive ORC serde tables (e.g., the ones created using the clause USING HIVE OPTIONS (fileFormat 'ORC')), Native implementation supports a vectorized ORC reader and has been the default ORC implementation since Spark 2.3. Since Spark 3.1.0, SPARK-33480 removes this difference by supporting CHAR/VARCHAR from Spark-side. hive implementation is designed to follow Hive’s behavior and uses Hive SerDe.įor example, historically, native implementation handles CHAR/VARCHAR with Spark’s native String while hive implementation handles it via Hive CHAR/VARCHAR.native implementation is designed to follow Spark’s data source behavior like Parquet. ![]() Two implementations share most functionalities with different design goals. Spark supports two ORC implementations ( native and hive) which is controlled by. Update tables using a load job in the datasets that you create.įor more information on IAM roles and permissions inīigQuery, see Predefined roles and permissions.Apache ORC is a columnar format which has more advanced features like native zstd compression, bloom filter and columnar encryption.
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